dd-l1-capetown-clinics-bbox

Status: done
Score: 1.000
Duration: 0:42
Cost: 0.17¢
Model: google/gemma-4-26b-a4b-it

Map (reference ← swipe → agent)

0:00
I'm about to push the `capetown_clinics.csv` export through to our case-management system and I want a quick inventory check first. Can you produce a small JSON file with the total clinic count, the overall bounding box as a bbox array, and the count per subdistrict, so I can confirm every health subdistrict is represented? Please write it to `clinic_inventory.json` with keys `count`, `bbox`, and `count_per_subdistrict`.
0:00
System info 
{
  "adapter": "openrouter",
  "model": "google/gemma-4-26b-a4b-it",
  "base_url": "https://openrouter.ai/api/v1"
}
0:03
Bash ls -F 
capetown_clinics.csv
0:08
Bash head -n 5 capetown_clinics.csv 
clinic_id,name,subdistrict,wkt_geom
1,Mokoena Western Clinic,Western,POINT(18.478309 -33.888756)
2,Ndlovu Western Clinic,Western,POINT(18.421127 -33.924929)
3,Pieterse Western Clinic,Western,POINT(18.389237 -33.976099)
4,van der Merwe Western Clinic,Western,POINT(18.47354 -33.890933)
0:19
Write /work/solve.py
content · 40 lines · py 
import pandas as pd
import json
from shapely import wkt

def main():
    # Load the data
    df = pd.read_csv('capetown_clinics.csv')
    
    # Parse geometry
    df['geometry'] = df['wkt_geom'].apply(wkt.loads)
    
    # Total count
    total_count = len(df)
    
    # Bounding box
    # Combine all geometries to get the overall bounds
    all_points = df['geometry'].tolist()
    minx = min(p.x for p in all_points)
    miny = min(p.y for p in all_points)
    maxx = max(p.x for p in all_points)
    maxy = max(p.y for p in all_points)
    bbox = [minx, miny, maxx, maxy]
    
    # Count per subdistrict
    count_per_subdistrict = df['subdistrict'].value_counts().to_dict()
    
    # Create output dictionary
    output = {
        "count": int(total_count),
        "bbox": bbox,
        "count_per_subdistrict": {k: int(v) for k, v in count_per_subdistrict.items()}
    }
    
    # Write to JSON
    with open('clinic_inventory.json', 'w') as f:
        json.dump(output, f, indent=4)

if __name__ == "__main__":
    main()

Wrote /work/solve.py (1036 chars).
0:25
Bash python solve.py && ls -F 
capetown_clinics.csv
clinic_inventory.json
solve.py
0:30
Read /work/clinic_inventory.json 
     1	{
     2	    "count": 80,
     3	    "bbox": [
     4	        18.380309,
     5	        -34.073855,
     6	        18.819549,
     7	        -33.701317
     8	    ],
     9	    "count_per_subdistrict": {
    10	        "Western": 12,
    11	        "Southern": 12,
    12	        "Tygerberg": 11,
    13	        "Northern": 10,
    14	        "Eastern": 10,
    15	        "Klipfontein": 9,
    16	        "Mitchells Plain": 8,
    17	        "Khayelitsha": 8
    18	    }
    19	}
0:40